The Beam Team - Stebner Labs


Education

  • Ph.D. (Mechanical Engineering), Northwestern University, 2012
  • M.S. (Mechanical Engineering), The University of Akron, 2008
  • B.S. (Mechanical Engineering), The University of Akron, 2005

Teaching Interests

Professor Stebner’s teaching interests include foundational courses in mechanical engineering with an emphasis on experimental methods, material behavior, mechanics, and artificial intelligence. He is engaged in educating both undergraduate and graduate students, fostering a comprehensive understanding of material deformation and mechanics. His approach integrates theory with practical experimentation to develop students' analytical and problem-solving skills in mechanics of materials, materials characterization, and machine learning.

Research Interests

Professor Stebner’s research is cross-disciplinary work that bridges mechanical engineering, materials science, and data science such as integrating data informatics and machine learning to accelerate discovery, development, and optimization of additive manufacturing technologies and developing new characterization and data analysis capabilities for in situ multi-scale studies of the mechanics and manufacturing of materials. It also incorporates the latest fundamental scientific discoveries into practical, usable tools for innovating engineering applications for companies and the government.

Recent Publications

  • S Mohan, H Holberton, AP Stebner, DC Hofmann, Assessing the Powder Bed Fusion–Laser Beam Potential of Glass‐Forming Alloys Using Single and Multitrack Laser Glazing Experiments, Advanced Engineering Materials 27 (9), 2402214, 2025.
  • NJ Lies, ME Carroll, AP Stebner, Development of a drop-casting fixture to improve microstructure repeatability and data pedigree of arc cast alloy research, International Journal of Refractory Metals and Hard Materials, 107386, 2025.
  • ST Goring, MB Pagan, AP Stebner, Development of spectral reflectometry characterization toward automation of polishing during sample preparation, Materials Characterization, 115726, 2025.
  • JS Weeks, AP Stebner, Efficient Multiscale Simulations of Incremental Sheet Forming Using Machine Learning Surrogate Models for Crystal Plasticity, Integrating Materials and Manufacturing Innovation, 1-20, 2025.
  • T Chattopadhyay, F Ceschin, ME Garza, D Zyunkin, A Chhotaray, ..., One Video to Steal Them All: 3D-Printing IP Theft through Optical Side-Channels, Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications …, 2025.